Some improvements to run affinity clustering on larger dataset and

compute density.

clustering/Makefile

@@ -0,0 +1,4 @@
+srun_cdsc='srun -p comdata-int -A comdata --time=300:00:00 --time-min=00:15:00 --mem=100G --ntasks=1 --cpus-per-task=28'
+affinity/subreddit_comment_authors_10000.feather:clustering.py /gscratch/comdata/output/reddit_similarity/subreddit_comment_authors_10000.parquet
+#	$srun_cdsc python3
+	clustering.py /gscratch/comdata/output/reddit_similarity/subreddit_comment_authors_10000.feather affinity/subreddit_comment_authors_10000.feather ---max_iter=400 --convergence_iter=15 --preference_quantile=0.85 --damping=0.85

clustering/clustering.py

@@ -1,12 +1,15 @@
+#!/usr/bin/env python3
+
 import pandas as pd
 import numpy as np
 from sklearn.cluster import AffinityPropagation
 import fire
 
-def affinity_clustering(similarities, output, damping=0.5, max_iter=100000, convergence_iter=30, preference_quantile=0.5, random_state=1968):
+def affinity_clustering(similarities, output, damping=0.9, max_iter=100000, convergence_iter=30, preference_quantile=0.5, random_state=1968, verbose=True):
     '''
     similarities: feather file with a dataframe of similarity scores
     preference_quantile: parameter controlling how many clusters to make. higher values = more clusters. 0.85 is a good value with 3000 subreddits.
+    damping: parameter controlling how iterations are merged. Higher values make convergence faster and more dependable. 0.85 is a good value for the 10000 subreddits by author. 
     '''
 
     df = pd.read_feather(similarities)
@@ -16,6 +19,8 @@ def affinity_clustering(similarities, output, damping=0.5, max_iter=100000, conv
 
     preference = np.quantile(mat,preference_quantile)
 
+    print(f"preference is {preference}")
+
     print("data loaded")
 
     clustering = AffinityPropagation(damping=damping,
@@ -24,6 +29,7 @@ def affinity_clustering(similarities, output, damping=0.5, max_iter=100000, conv
                                      copy=False,
                                      preference=preference,
                                      affinity='precomputed',
+                                     verbose=verbose,
                                      random_state=random_state).fit(mat)
 
 

density/Makefile

@@ -0,0 +1,7 @@
+all: /gscratch/comdata/output/reddit_density/comment_terms_10000.feather /gscratch/comdata/output/reddit_density/comment_authors_10000.feather
+
+/gscratch/comdata/output/reddit_density/comment_terms_10000.feather:overlap_density.py /gscratch/comdata/output/reddit_similarity/comment_terms_10000.feather /gscratch/comdata/output/reddit_similarity/comment_terms_10000.feather
+	python3 overlap_density.py terms --inpath="/gscratch/comdata/output/reddit_similarity/comment_terms_10000.feather" --outpath="/gscratch/comdata/output/reddit_density/comment_terms_10000.feather" --agg=pd.DataFrame.sum
+
+/gscratch/comdata/output/reddit_density/comment_authors_10000.feather:overlap_density.py /gscratch/comdata/output/reddit_similarity/comment_authors_10000.feather
+	python3 overlap_density.py authors --inpath="/gscratch/comdata/output/reddit_similarity/comment_authors_10000.feather" --outpath="/gscratch/comdata/output/reddit_density/comment_authors_10000.feather" --agg=pd.DataFrame.sum

density/overlap_density.py

@@ -0,0 +1,57 @@
+import pandas as pd
+from pandas.core.groupby import DataFrameGroupBy as GroupBy
+import fire
+import numpy as np
+
+def overlap_density(inpath, outpath, agg = pd.DataFrame.sum):
+    df = pd.read_feather(inpath)
+    df = df.drop('subreddit',1)
+    np.fill_diagonal(df.values,0)
+    df = agg(df, 0).reset_index()
+    df = df.rename({0:'overlap_density'},axis='columns')
+    df.to_feather(outpath)
+    return df
+
+def overlap_density_weekly(inpath, outpath, agg = GroupBy.sum):
+    df = pd.read_parquet(inpath)
+    # exclude the diagonal
+    df = df.loc[df.subreddit != df.variable]
+    res = agg(df.groupby(['subreddit','week'])).reset_index()
+    res.to_feather(outpath)
+    return res
+
+def author_overlap_density(inpath="/gscratch/comdata/output/reddit_similarity/comment_authors_10000.feather",
+                           outpath="/gscratch/comdata/output/reddit_density/comment_authors_10000.feather", agg=pd.DataFrame.sum):
+    if type(agg) == str:
+        agg = eval(agg)
+
+    overlap_density(inpath, outpath, agg)
+
+def term_overlap_density(inpath="/gscratch/comdata/output/reddit_similarity/comment_terms_10000.feather",
+                         outpath="/gscratch/comdata/output/reddit_density/comment_term_similarity_10000.feather", agg=pd.DataFrame.sum):
+
+    if type(agg) == str:
+        agg = eval(agg)
+
+    overlap_density(inpath, outpath, agg)
+
+def author_overlap_density_weekly(inpath="/gscratch/comdata/output/reddit_similarity/subreddit_authors_10000_weekly.parquet",
+                                  outpath="/gscratch/comdata/output/reddit_density/comment_authors_10000_weekly.feather", agg=GroupBy.sum):
+    if type(agg) == str:
+        agg = eval(agg)
+
+    overlap_density_weekly(inpath, outpath, agg)
+
+def term_overlap_density_weekly(inpath="/gscratch/comdata/output/reddit_similarity/comment_terms_10000_weekly.parquet",
+                                outpath="/gscratch/comdata/output/reddit_density/comment_terms_10000_weekly.parquet", agg=GroupBy.sum):
+    if type(agg) == str:
+        agg = eval(agg)
+
+    overlap_density_weekly(inpath, outpath, agg)
+
+
+if __name__ == "__main__":
+    fire.Fire({'authors':author_overlap_density,
+               'terms':term_overlap_density,
+               'author_weekly':author_overlap_density_weekly,
+               'term_weekly':term_overlap_density_weekly})

old/.#tfidf_authors.py

@@ -1 +0,0 @@
-nathante@n2347.hyak.local.31061:1602221800

old/.#tfidf_comments_weekly.py

@@ -1 +0,0 @@
-nathante@n2347.hyak.local.31061:1602221800

similarities/#cosine_similarities.py#

@@ -1,73 +0,0 @@
-from pyspark.sql import functions as f
-from pyspark.sql import SparkSession
-import pandas as pd
-import fire
-from pathlib import Path
-from similarities_helper import prep_tfidf_entries, read_tfidf_matrix, select_topN_subreddits
-
-
-def cosine_similarities(infile, term_colname, outfile, min_df=None, included_subreddits=None, topN=500, exclude_phrases=False):
-    spark = SparkSession.builder.getOrCreate()
-    conf = spark.sparkContext.getConf()
-    print(outfile)
-    print(exclude_phrases)
-
-    tfidf = spark.read.parquet(infile)
-
-    if included_subreddits is None:
-        included_subreddits = select_topN_subreddits(topN)
-    else:
-        included_subreddits = set(open(included_subreddits))
-
-    if exclude_phrases == True:
-        tfidf = tfidf.filter(~f.col(term_colname).contains("_"))
-
-    print("creating temporary parquet with matrix indicies")
-    tempdir = prep_tfidf_entries(tfidf, term_colname, min_df, included_subreddits)
-    tfidf = spark.read.parquet(tempdir.name)
-    subreddit_names = tfidf.select(['subreddit','subreddit_id_new']).distinct().toPandas()
-    subreddit_names = subreddit_names.sort_values("subreddit_id_new")
-    subreddit_names['subreddit_id_new'] = subreddit_names['subreddit_id_new'] - 1
-    spark.stop()
-
-    print("loading matrix")
-    mat = read_tfidf_matrix(tempdir.name, term_colname)
-    print('computing similarities')
-    sims = column_similarities(mat)
-    del mat
-    
-    sims = pd.DataFrame(sims.todense())
-    sims = sims.rename({i:sr for i, sr in enumerate(subreddit_names.subreddit.values)}, axis=1)
-    sims['subreddit'] = subreddit_names.subreddit.values
-
-    p = Path(outfile)
-
-    output_feather =  Path(str(p).replace("".join(p.suffixes), ".feather"))
-    output_csv =  Path(str(p).replace("".join(p.suffixes), ".csv"))
-    output_parquet =  Path(str(p).replace("".join(p.suffixes), ".parquet"))
-
-    sims.to_feather(outfile)
-    tempdir.cleanup()
-
-def term_cosine_similarities(outfile, min_df=None, included_subreddits=None, topN=500, exclude_phrases=False):
-    return cosine_similarities('/gscratch/comdata/output/reddit_similarity/tfidf/comment_terms.parquet',
-                               'term',
-                               outfile,
-                               min_df,
-                               included_subreddits,
-                               topN,
-                               exclude_phrases)
-
-def author_cosine_similarities(outfile, min_df=2, included_subreddits=None, topN=10000):
-    return cosine_similarities('/gscratch/comdata/output/reddit_similarity/tfidf/comment_authors.parquet',
-                               'author',
-                               outfile,
-                               min_df,
-                               included_subreddits,
-                               topN,
-                               exclude_phrases=False)
-
-if __name__ == "__main__":
-    fire.Fire({'term':term_cosine_similarities,
-               'author':author_cosine_similarities})
-

similarities/.#cosine_similarities.py

@@ -1 +0,0 @@
-nathante@n2347.hyak.local.31061:1602221800

similarities/.#tfidf_weekly.py

@@ -1 +0,0 @@
-nathante@n2347.hyak.local.31061:1602221800

similarities/Makefile

@@ -1,2 +1,5 @@
 /gscratch/comdata/output/reddit_similarity/subreddit_comment_authors_10000.parquet: cosine_similarities.py /gscratch/comdata/output/reddit_similarity/tfidf/comment_authors.parquet
-	start_spark_and_run.sh 1 cosine_similarities.py author --outfile=/gscratch/comdata/output/reddit_similarity/subreddit_comment_authors_10000.parquet
+	start_spark_and_run.sh 1 cosine_similarities.py author --outfile=/gscratch/comdata/output/reddit_similarity/subreddit_comment_authors_10000.feather
+
+/gscratch/comdata/output/reddit_similarity/comment_terms_10000_weekly.parquet: cosine_similarities.py /gscratch/comdata/output/reddit_similarity/tfidf/comment_authors.parquet
+	start_spark_and_run.sh 1 weekly_cosine_similarities.py term --outfile=/gscratch/comdata/output/reddit_similarity/subreddit_comment_terms_10000_weely.parquet

similarities/cosine_similarities.py

@@ -3,7 +3,7 @@ from pyspark.sql import SparkSession
 import pandas as pd
 import fire
 from pathlib import Path
-from similarities_helper import prep_tfidf_entries, read_tfidf_matrix, select_topN_subreddits
+from similarities_helper import prep_tfidf_entries, read_tfidf_matrix, select_topN_subreddits, column_similarities
 
 
 def cosine_similarities(infile, term_colname, outfile, min_df=None, included_subreddits=None, topN=500, exclude_phrases=False):

similarities/job_script.sh

@@ -1,4 +1,4 @@
 #!/usr/bin/bash
 start_spark_cluster.sh
-spark-submit --master spark://$(hostname):18899 cosine_similarities.py author --outfile=/gscratch/comdata/output/reddit_similarity/subreddit_comment_authors_10000.parquet
+spark-submit --master spark://$(hostname):18899 weekly_cosine_similarities.py term --outfile=/gscratch/comdata/output/reddit_similarity/subreddit_comment_terms_10000_weely.parquet
 stop-all.sh

similarities/tfidf.py

@@ -45,7 +45,7 @@ def tfidf_terms(outpath='/gscratch/comdata/output/reddit_similarity/tfidf/commen
                  []
                  )
 
-def tfidf_authors_weekly(outpath='/gscratch/comdata/output/reddit_similarity/tfidf/comment_authors.parquet',
+def tfidf_authors_weekly(outpath='/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_authors.parquet',
                   topN=25000):
 
     return tfidf_weekly("/gscratch/comdata/output/reddit_ngrams/comment_authors.parquet",
@@ -55,7 +55,7 @@ def tfidf_authors_weekly(outpath='/gscratch/comdata/output/reddit_similarity/tfi
                  ['[deleted]','AutoModerator']
                  )
 
-def tfidf_terms_weekly(outpath='/gscratch/comdata/output/reddit_similarity/tfidf/comment_terms.parquet',
+def tfidf_terms_weekly(outpath='/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_terms.parquet',
                 topN=25000):
 
     return tfidf_weekly("/gscratch/comdata/output/reddit_ngrams/comment_terms.parquet",

similarities/weekly_cosine_similarities.py

@@ -35,7 +35,7 @@ def cosine_similarities_weekly(tfidf_path, outfile, term_colname, min_df = None,
     subreddit_names['subreddit_id_new'] = subreddit_names['subreddit_id_new'] - 1
     spark.stop()
 
-    weeks = list(subreddit_names.week.drop_duplicates())
+d    weeks = sorted(list(subreddit_names.week.drop_duplicates()))
     for week in weeks:
         print(f"loading matrix: {week}")
         mat = read_tfidf_matrix_weekly(tempdir.name, term_colname, week)